Deep Reinforcement Learning Atari: how does the agent understand motion?
In the article Playing Atari with Deep Reinforcement Learning, Mnih et al, 2013, which was a major outbreak in Deep Reinforcement learning (especially in Deep Q learning), they don't feed only the last image to the network. They stack the 4 last images :

For the experiments in this paper, the function φ from algorithm 1
applies this preprocessing to the last 4 frames of a history and stacks
them to produce the input to the Q-function

So they add the motion through sequentiality. From various articles and own coding experiences, this seems to me to be the main common approach. I don't know if other techniques have been implemented.
One thing we could imagine would be to compute the Cross-correlation between a previous frame and the last one, and then feed the cross correlation product to the net.
Another idea would be to train previously a CNN to extract motion features from a sequence of frames, and feed these extracted features to your net. This article (Performing Particle Image Velocimetry using Artificial Neural Networks: a proof-of-concept), Rabault et al, 2017 is an example of a CNN to extract motion features.